Phytoestrogen content of beverages, nuts, seeds, and oils

Phytoestrogens are secondary plant metabolites that have received increasing attention for their bioactivity, in particular due to their structural and functional similarity to 17beta-estradiol. Although urinary and plasma phytoestrogens can be used as biomarkers for dietary intake, this is often not possible in large epidemiological studies or in the assessment of general exposure in free-living individuals. Accurate information about dietary phytoestrogens is therefore important, but there are very limited data concerning food contents. In this study was analyzed a comprehensive selection of tea, coffee, alcoholic beverages, nuts, seeds, and oils for their phytoestrogen content using a newly developed sensitive method based on LC-MS incorporating (13)C 3-labeled standards. Phytoestrogens were detected in all foods analyzed, although the contents in gin and bitter (beer) were below the limit of quantification (1.5 microg/100 g). Lignans were the main type of phytoestrogens detected. Tea and coffee contained up to 20 microg/100 g phytoestrogens and beer (except bitter) contained up to 71 microg/100 g, mainly lignans. As these beverages are commonly consumed, they are a main source of dietary lignans. The results published here will contribute to databases of dietary phytoestrogen content and allow a more accurate determination of phytoestrogen exposure in free-living individuals.     

A three-phase sampling strategy for large-scale multiresource forest inventories

This article considers a two-phase estimation for the areal extent of K land categories partitioning a study region and a three-phase estimation for the biomass of W forest categories out of the K. In the first phase, a sample of N points is selected according to the unaligned systematic sampling. In the second phase, the selected points are partitioned into L strata on the basis of aerial photos. Then, a total sample of n < N points is selected by stratified sampling and the selected points are visited on the ground and correctly classified into one of K categories. The information achieved in the second phase is sufficient for obtaining an unbiased estimator of the areal extent vector together with a conservative estimator of its variance-covariance matrix. As to the estimation of the biomass of the W forest categories, in the third phase the second-phase sample is further partitioned into substrata on the basis of ground information. Finally, a total sample of m < n points is selected by stratified sampling. Then a plot of adequate radius centered at each point is considered and the biomass is recorded within. An unbiased estimator of the biomass vector is derived together with a conservative estimator of its variance-covariance matrix. The proposed strategy also makes it possible to obtain the calibrated estimator of the areal extent vector as well as estimators for the sums or ratios of the areal extents and biomasses. The application of the strategy in the Italian National Forest Inventory is considered.